Track accepted paper

CiteScore:
2.72ℹCiteScore:2019: 2.720CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor:
2.189ℹImpact Factor:2018: 2.189The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
Journal Citation Reports (Clarivate Analytics, 2019)

5-Year Impact Factor:
2.799ℹFive-Year Impact Factor:2018: 2.799To calculate the five year Impact Factor, citations are counted in 2018 to the previous five years and divided by the source items published in the previous five years.
Journal Citation Reports (Clarivate Analytics, 2019)

Source Normalized Impact per Paper (SNIP):
1.117ℹSource Normalized Impact per Paper (SNIP):2018: 1.117SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR):
1.382ℹSCImago Journal Rank (SJR):2018: 1.382SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

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The Latest Mendeley Data Datasets for Journal of Behavior Therapy and Experimental Psychiatry

Mendeley Data Repository is free-to-use and open access. It enables you to deposit any research data (including raw and processed data, video, code, software, algorithms, protocols, and methods) associated with your research manuscript. Your datasets will also be searchable on Mendeley Data Search, which includes nearly 11 million indexed datasets. For more information, visit Mendeley Data.

study 1-
First, we excluded participants with abnormally fast reaction times (less than 3 standard deviation below average; n = 12) and with unconscientious response patterns (i.e., did not respond appropriately to a test video included in the task; n = 18). Thus, the final sample consisted of 147 participants (76 women, mean age = 35, SD = 10.23).
Next, we divided the participants into two groups based on their SA scores. Individuals in the LSA groups were those whose LSAS score was less than 30 and whose SPIN score was less than 10 (n = 40, 17 women). The HSA group included participants with LSAS scores above 60 and SPIN score above 30 (n = 39, 26 women) (for SA cut-off in LSAS and SPIN see Rytwinski et al., 2008; Masia-Warner et al., 2003). Means of SPIN and LSAS scores in the LSA group were 3.3 (SD = 3.40) and 13.0 (SD = 9.60) respectively. Means of SPIN and LSAS scores in the HSA group were 42.1 (SD = 10.33) and 87.3 (SD = 13.74) respectively. Thus, the HSA group included individuals with SA levels equivalent to those seen in treatment-seeking population (Rytwinski et al., 2008; Ranta et al., 2007; Heurer et al., 2010). Our main analyses were based on 2 (Group: HSA vs. LSA) X 2 (Condition: Exclusion vs. Inclusion) between-subject ANOVAs on the mean (across all 6 clips) recognition of transition times
study 2-
Our data analytic strategy was conceptually identical to our strategy in Study 1. First, twenty-one participants were excluded from the experiment due to an extremely fast reaction times to the video clips (responses that were faster than 3 standard deviation below group average). Forty participants failed to perform the task in a conscientious manner. These criteria resulted in a final sample of 198 participants (107 women, mean age: 35 SD= 11.58). Next, participants were divided into HSA (n = 44, 27 women) and LSA (n = 47, 23 women) using the same criteria. Means of SPIN and LSAS in the LSAs group were 3.00 (SD = 2.52) and 14.40 (SD = 8.56) respectively. Means of SPIN and LSAS in the HSA group were 40.60 (SD = 10.21) and 80.50 (SD = 14.00) respectively. Again, the HSA group contained individuals with SA levels equivalent to the levels of individuals seeking treatment for their SA difficulties.

Background and Objectives: Biomedical explanations of psychiatric problems, compared to psychosocial explanations, may amplify psychiatric stigma. One limitation of existing research is the measurement of almost exclusively self-reported stigma. This study evaluated the stigma-related effects of biomedical versus psychosocial explanations of schizophrenia using conventional self-report and two other measurement approaches that may tap more deeply held attitudes.
Methods: One hundred three undergraduates listened to a vignette describing a man with (1) schizophrenia of biomedical origin, (2) schizophrenia of psychosocial origin, or (3) diabetes. They then completed an Implicit Association Test, conventional self-report stigma measures, and projected other measures that captured perceptions of most other people’s likely impressions.
Results: Participants were more likely to attribute stigmatizing views to others compared to themselves. The projected other measurement, but not the conventional self-report measurement, predicted implicit attitudes. We obtained no evidence that the psychosocial causal explanation of schizophrenia led to decreased stigma compared to the biomedical causal explanation. In fact, the psychosocial causal explanation increased stereotyped attitudes.
Limitations: The absence of a schizophrenia control group complicates interpretation of biomedical versus psychosocial group comparisons.
Conclusions: Psychosocial causal explanations that portray people as subject to numerous, severe stressors may evoke the cultural stereotype of the “ticking time bomb” from whom others seek safe distance.